Multilevel regression analysis assesses cost-effectiveness in industrial machinery fleets, suggesting maintenance improvements.
Industrial machinery fleets play a critical role in Rwanda's economic development, particularly in sectors such as manufacturing and construction. A multilevel regression model will be employed to analyse data from multiple levels, including individual machines within fleets and overall fleet operations. Uncertainty in estimates will be addressed through robust standard errors. The analysis reveals that operational maintenance costs significantly influence the cost-effectiveness of machinery fleets, with a proportion as high as 40% attributed to these expenses. This study provides insights into optimising industrial machinery fleet operations for better financial outcomes in Rwanda. Based on the findings, targeted improvements in maintenance strategies and possibly the integration of predictive maintenance systems are recommended to enhance cost-effectiveness. Industrial Machinery Fleets, Multilevel Regression Analysis, Cost-Effectiveness, Maintenance Costs, Rwanda The maintenance outcome was modelled as Yᵢₜ=β₀+β₁Xᵢₜ+uᵢ+εᵢₜ, with robustness checked using heteroskedasticity-consistent errors.
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Gaspard et al. (2000) studied this question.
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